Convolutional Neural Network (CNN) is transforming the field of medical diagnosis. CNN can help doctors make faster, more accurate diagnosis by providing automatic learning techniques for predicting the common patterns from the medical image data. Human expert provides limited interpretation of medical images due to its subjectivity, complexity and extensive variations across the image. CNN is able to provide state of the art solutions with good accuracy for medical imaging and is powered by the increasing availability of healthcare data. Major disease areas that use CNN includes cancer, dermatology, neurology and cardiology. This paper focuses on the use of different CNN architectures based on their performance for accurate medical diagnosis. We also discuss the current status of CNN applications in healthcare and its various limitations.